Evaluation is the hardest part with respect to clustering.
If you knew what you are looking for, you would not need to run cluster analysis.
So there is no such thing as an objective "truth" for clustering. What you consider a cluster depends a lot of what your personal needs are, and unless you encode them into a custom algorithm, chances are that the clustering algorithm computes something entirely different.
k-means for example minimizes the variances. Whether or not variance agrees with your idea of a cluster!
For your use case, the best sanity check is that each of the existing genre assignments should be mostly within one of the clusters. If it's all over the place, the clustering does not cluster by your notion of genres.