  Recent cosmological hydrodynamical simulations are able to reproduce numerous
statistical properties of galaxies that are consistent with observational data.
Yet, the adopted subgrid models strongly affect the simulation outcomes,
limiting the predictive power of these simulations. In this work, we perform a
suite of isolated galactic disk simulations under the {\it SMUGGLE} framework
and investigate how different subgrid models affect the properties of giant
molecular clouds (GMCs). We employ {\sc astrodendro}, a hierarchical
clump-finding algorithm, to identify GMCs in the simulations. We find that
different choices of subgrid star formation efficiency, $\epsilon_{\rm ff}$,
and stellar feedback channels, yield dramatically different mass and spatial
distributions for the GMC populations. Without feedback, the mass function of
GMCs has a shallower power-law slope and extends to higher mass ranges compared
to runs with feedback. Moreover, higher $\epsilon_{\rm ff}$ results in faster
molecular gas consumption and steeper mass function slopes. Feedback also
suppresses power in the two-point correlation function (TPCF) of the spatial
distribution of GMCs. Specifically, radiative feedback strongly reduces the
TPCF on scales below 0.2~kpc, while supernova feedback reduces power on scales
above 0.2~kpc. Finally, runs with higher $\epsilon_{\rm ff}$ exhibit a higher
TPCF than runs with lower $\epsilon_{\rm ff}$, because the dense gas is
depleted more efficiently thereby facilitating the formation of well-structured
supernova bubbles. We argue that comparing simulated and observed GMC
populations can help better constrain subgrid models in the next-generation of
galaxy formation simulations.
