Is it necessary to do confirmatory factor analysis for structure equation modelling? I have done Principal Component Analysis for a scale comprising multiple latent variables. Is it necessary to do Confirmatory Factor Analysis before doing Structure Equation Modelling?
 A: Discussions of whether you should use PCA to model exploratory latent variables (or not) aside, the short answer here is: typically, yes. PCA has given you a data-driven exploratory model, but in advance of modelling the structural associations between latent variables, editors, reviewers, and readers will typically want to see that the measurement model the data "gave" you replicates, when you deliberately fit it (with a discriminating pattern of factor loadings) in a separate sample. 
There are a couple of notable exceptions to this standard practice, the first of which (ESEM) might be something for you to consider:
Exploratory Structural Equation Modeling
Want to model the structural parameters of exploratory latent constructs? ESEM (Asparouohov & Muthén, 2009) may be for you! However, ESEM uses common factor measurement models--not PCA models as you have done. It's also worth noting that at this point, accessible software capable of ESEM is relegated to Mplus, and to a more limited extent, Revelle's psyc package for R. There's also a gap in the simulation literature with respect to ESEM models; whereas much is known about evaluating the fit of traditional SEM/CFA models, ESEM models (which are deliberately less parsimonious) have received little attention. 
Parcelling
At some point in time, a measurement model may be so widely replicated that it no longer seems worth fitting the full measurement model in all of its complexity (which can degrade model fit). Researchers in this position may wish to parcel their indicators (see Little et al., 2002 for a discussion), so that their latent variables are locally just-identified, and thereby, their measurement does not substantially detract from the fit of the overall model. Instead, overall model fit is primarily driven by the adequacy of the representation of the structural associations between latent variables. 
In your case, however, parcelling would not be appropriate because your measurement model is still entirely untested--it hasn't been fit with a confirmatory approach even once, so parcelling would be very questionable. Further, parcelling isn't a practice without its critics--even under the strict conditions I've characterized (and described in Little et al. 2002). I'd recommend those interested in the parceling debate to read the recent review by Sterba and Rights (2017), but in a nutshell, they take issue with the extent to which how a researcher decides to combine indicators in parcels can influence model fit, tests of structural parameters, etc.
References
Asparouhov, T., & Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling, 16, 397-438. doi: 10.1080/10705510903008204
Little, T.D., Cunningham, W. A., Shahar, G., & Widaman, K. F. (2002). To parcel or not to parcel: Exploring the question, weighing the merits. Structural Equation Modeling, 9, 151-173. doi: 10.1207/S15328007SEM0902_1
Sterba, S. K., & Rights, J. D. (2017). Effects of parcelling on model selection: Parcel-allocation variability in model ranking. Psychological  Methods, 22, 47-68. doi: 10.1037/met0000067
A: Yes, confirmatory factor analysis must be done for the internal consistency of the paragraphs of the questionnaire, and to also indicate what paragraphs negatively affected the scale.
