Does the number of samples matter for FFT, and how to get a specific frequency visible? If I have a signal that has 740 samples, the sampling frequency is 1000 Hz, and I take an FFT of length 1024, are the frequencies I get now 0, fs/1024, 2*fs/1000, ..., 511*fs/1024? I mean, do I get this correctly: the frequency resolution does not depend on the number of samples I have, only FFT length and sampling frequency. If I had 1480 samples, the resolution would be still the same? This makes my head hurt.

Next, suppose that the signal I have has an interesting frequency at 26 Hz (strictly that one frequency). How do I get it appear properly in the frequency spectrum (my frequency bins are spaced 1000/1024 = 0.9766 Hz apart, so am I at dead end?).
The "information content" is related to the number of samples.  The length of the FFT merely interpolates the spectral frequency curve represented by the number of samples.  If an FFT result bin doesn't end up exactly centered on some desired frequency, you can interpolate it from the nearest bins, using a high-quality (Sinc kernel, et.al.) interpolator, since FFT bins have a greater-than-zero bandwidth (the shape of the transform of the default rectangular or other window).  Using a longer zero-padded FFT gives you more interpolation points, and thus a denser looking point plot, but no better spectral separation resolution than provided by the number of actual samples.