Blind deconvolution is an ubiquitous non-linear inverse problem in applications like wireless communications and image processing. This problem is generally ill-posed since signal identifiability is a key concern, and there have been efforts to use sparse models for regularizing blind deconvolution to promote signal identifiability. Part I of this two-part paper establishes a measure theoretically tight characterization of the ambiguity space for blind deconvolution and unidentifiability of this inverse problem under unconstrained inputs. Part II of this paper analyzes the identifiability of the canonical-sparse blind deconvolution problem and establishes surprisingly strong negative results on the sparsity-ambiguity trade-off scaling laws. Specifically, the ill-posedness of canonical-sparse blind deconvolution is quantified by exemplifying the dimension of the unidentifiable signal sets. An important conclusion of the paper is that canonical sparsity occurring naturally in applications is insufficient and that coding is necessary for signal identifiability in blind deconvolution. The methods developed herein are applied to a second-hop channel estimation problem to show that a family of subspace coded signals (including repetition coding and geometrically decaying signals) are unidentifiable under blind deconvolution.