I am particularly interested in the pzhegvx and pzheevx routines with $n \approx 1000$ and the pzgemm and pzherk routines with $m,n \approx 1000$ and $k \approx 100$. These are the lower bounds in the space of reasonable inputs. Otherwise, as Jeff points out, scalapack wouldn't make a whole lot of sense.
For matrices of that size, I'm not sure if you want to use ScaLAPACK at all.
If you've got the ScaLAPACK code already, it shouldn't be hard to implement your own logic to drop into LAPACK instead. At the very least, doing that will allow you to perform the experiments required to answer your own question.
Not well. If serial is a common case, it is important to wrap and drop down to lapack for serial execution.
I implemented this in my code. For 2013 MKL pzhegvx with $n \approx 100 (1000)$ seems to incur 30% (100%) overhead compared to zhegvx when executed in serial.
This seems high to me, so I'm a little worried about my implementation. Note that I inline zhegvx to enable re-use of the factorization of $B$.