Compressive sensing (CS) based Inverse Synthetic Aperture Radar (ISAR) imaging exploits the sparsity of the target scene to achieve high resolution and effective denoising with limited measurements. This paper extends the CS based ISAR imaging to further include the continuity structure of the target scene within a Bayesian framework. A correlated prior is imposed to statistically encourage the continuity structures in both the cross-range and range domains of the target region and the Gibbs sampling strategy is used for Bayesian inference. Because the resulted method requires to recover the whole target scene at a time with heavy computational complexity, an approximate strategy is proposed to alleviate the computational burden. Experimental results demonstrate that the proposed algorithm can achieve substantial improvements in terms of preserving the weak scatterers and removing noise over other reported CS based ISAR imaging algorithms.