How to predict the spectrum of an aliased/folded signal without looking at the FFT?
Your description of the situation is faulty.  A complex signal sampled at 64 MHz can represent frequencies from -32 MHz to +32 MHz, so the presence of tones at -25 MHz and +17MHz is fine.  However, when you decimate by two the sample rate drops to 32 MHz, and the frequency range becomes -16 MHz to +16 MHz.  In other words, you can't have a signal at -30 MHz afterwards.

The aliasing shifts the signals down to the new Nyquist zone (the region between the negative and positive Nyquist frequencies, -16 MHz and +16 MHz respectively).  -25 MHz is 9 MHz below the negative Nyquist frequency so it is shifted up by 32 MHz (the width of the Nyquist zone) to +7 MHz.  +17 MHz is 1 MHz above the positive Nyquist frequency, so it is shifted down by 32 MHz to -15 MHz.



The picture above shows the before (top plot) and after (bottom plot) frequency spectrum.  It was created with the following Matlab commands:

sig = exp(j*(1:1000)*2*pi*-25E6/64E6) + exp(j*(1:1000)*2*pi*17E6/64E6);
freq = -32E6:64E6/1000:32E6;
dec = sig(1:2:end);
freqDec = -16E6:64E6/1000:(16E6-1);
subplot(2,1,1)
plot(freq, 20*log10(abs(fftshift(fft(sig)))))
subplot(2,1,2)
plot(freqDec, 20*log10(abs(fftshift(fft(dec)))))


EDIT: I see that you largely had the right idea when you calculated the aliased frequencies.  The only mistake that you made was to not divide the frequencies by 2 due to the decimation.  Thus, -30 MHz should have been -15 MHz, and 14 MHz should have been 7 MHz.