If data in the treatment group is not normal while the control group is it sounds like the treatment may only be affecting a subset of the sample or having variable levels of effect. Comparing means under such circumstances would be losing out on this information. You should attempt to offer explanations for why this change of distribution occurred rather than only comparing means. The rank tests assume that both groups come from the same shape distribution. If you believe the distributions are different the tests are not useful for your purposes.
Let us take an example of what can happen with the U-test. We will make our control group come from a normal distribution with mean=0. Meanwhile the treatment will have negative effects on half the subjects and positive effects on the other half. So the treatment group will come from two normal distributions. The first with mean=-5, the second with mean=5. All distributions have sd=1 and both groups have sample size=100. Red shows the treatment group while blue shows the control group:
Results of doing a U-test (which is also called the Wilcoxon test):
Wilcoxon rank sum test with continuity correction
data: a and b
W = 4999, p-value = 0.999
alternative hypothesis: true location shift is not equal to 0
We can see it returns "not significant". Would you really want to conclude the treatment had no effect?
R code for generating the above:
control<-rnorm(100,0,1) # create control data
treatment<-c(rnorm(50,-5,1),rnorm(50,5,1)) # create treatment data
# Get min/max values (for plotting)
# make plots
hist(treatment, breaks=seq(min.val-.1,max.val+.1,.5), col="Red",
hist(control, add=T, breaks=seq(min.val-.1,max.val+.1,.5),col="Blue")