I suppose I would employ cophentic correlation to identify which of the linkage methods mentioned above produces the dendrogram with the highest similarity to the underlying distance matrix.
The distance between two objects in your dendrogram and their first horizontal link, i.e. the point at which they branch off into two different groups, is called their cophenetic distance. There are functions calculating these cophenetic distances for all pairs of n objects, which results in a matrix of dimensions n x n. Now correlate this matrix with the underlying manhattan distance matrix, for all the dendograms you have identified with your different linkage methods, and see which dendogram produces the highest correlation.
Additionally, you can employ Gower's distance, which is the sum of squares difference between the cophenetic distances calculated from your dendrograms, and the underlying distance matrix.
For reference, see section 4.7 in Numerical Ecology with R by Borcard, Gillet and Legendre, 2011 (ISBN 978-1-4419-7975-9).